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All Journal MEDIA KOMUNIKASI TEKNIK SIPIL Jurnal Pendidikan Teknik Sipil (JPTS) Jurnal Teknik Sipil Recent in Engineering Science and Technology
Edistria, Ega
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation

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Simulasi Model Sambungan Mekanis dengan Menggunakan Coupler Untuk Beton Pracetak Rosyidah, Anis; Edistria, Ega; Wijaya, Bunga Shafira
MEDIA KOMUNIKASI TEKNIK SIPIL Volume 29, Nomor 1, JULI 2023
Publisher : Department of Civil Engineering, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/mkts.v29i1.43164

Abstract

The use of mechanical joints can improve the performance of the connection and make the time more efficient. The purpose of this study was to determine the maximum tensile force, failure pattern and effect of epoxy thickness on the maximum tensile force, epoxy-bar bonding stress, and epoxy-coupler bonding stress of each splice type Grouted Coupler Connector. The research specimens were 6 pieces with varying thicknesses of epoxy and diameter of reinforcing steel. Software that supports the pullout test simulation is ANSYS and the research data processing method uses Simple Linear Regression Analysis. The output from the pullout test simulation is the maximum tensile force with a thickness of 25mm epoxy on the reinforcing steel D16, D22, and D25 of 91.156 kN; 148,090 kN, and 203,295 kN. All the test specimens have an epoxy coupler slip failure pattern. And the concluded from the simple linear regression analysis is a significant effect between the thickness of the epoxy on the maximum tensile force and bond stress, with a negative regression coefficient value. The optimum value of using thick epoxy with a varying diameter of reinforcing bars is 25mm.
Self-Protection Equipment Detection System in Heavy Weight Workshop of Politeknik Negeri Jakarta Using Artificial Intel-ligence Rezakusuma, Muhammad; Abdillah, Abdul Azis; Liliana, Dewi Yanti; Edistria, Ega; Arifin, Samsul; Muzakki, Zahran
Recent in Engineering Science and Technology Vol. 1 No. 01 (2023): RiESTech Volume 01 No. 01 Years 2023
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v1i01.4

Abstract

The creating process, how it works and the performance of the detection system using Artificial Intelligence. The development of this innovation contributes to the Heavy Equipment Workshop of the Jakarta State Polytechnic to detect the early potential for work accidents. The methods are device tuning, inputs, training models, performance, trials and outputs. The creating process and how the detection system works using Artificial Intelligence each has 3 steps and accuracy using 3 cameras, namely the internal webcam (1MP), the JETE external webcam (720P) and the Samsung Galaxy A22 mobile phone camera (13MP). The process of making this innovation has 3 steps, namely data input, export, file grouping. There are 3 steps to work, namely open the file, run and output. The result of the accuracy of the internal webcam is very low, the JETE external webcam is better than the internal webcam and the mobile phone camera is better than the JETE external webcam.
Comparative Analysis of Regression Methods for Estimation of Remaining Useful Life of Lithium Ion Battery Assagaf, Idrus; Abdillah, Abdul Azis; Edistria, Ega; Sukandi, Agus; Prasetya, Sonki; Apriana, Asep; Nugroho; Kamil, Raihan
Recent in Engineering Science and Technology Vol. 3 No. 01 (2025): RiESTech Volume 03 No. 01 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i01.93

Abstract

Lithium batteries play a critical role in modern technological applications, including electric vehicles and portable electronic devices. Ensuring accurate estimation of their remaining useful life is essential to improve system efficiency and reliability. This study focuses on predicting the remaining useful life of lithium batteries using advanced regression methods. Data were collected from lithium battery charge-discharge cycles, encompassing key operational parameters such as voltage, current, and temperature. The analysis employed several regression models, including linear regression, lasso regression, and Ridge regression, to identify relationships between these parameters and battery life. The models were evaluated based on estimation accuracy, with Root Mean Square Error (RMSE) as the primary performance metric. The findings demonstrate that regression methods can effectively capture non-linear relationships between input variables and the remaining useful life, with lasso and Ridge regression showing superior performance in reducing prediction errors. These results underscore the potential of regression-based approaches in providing robust and reliable estimations of battery life. The conclusions highlight the importance of these models for developing predictive battery management systems, which can optimize battery performance and extend their operational lifespan across various applications. This research establishes a solid foundation for future studies on intelligent battery health monitoring and management.
OPTIMASI MATERIAL DOUBLE SKIN FACADE TERHADAP PENURUNAN NILAI OTTV PADA GEDUNG KANTOR PUSAT ASDP INDONESIA FERRY Kirana, Puspanendah Sasotya; Nurwidyaningrum, Dyah; Edistria, Ega
JURNAL TEKNIK SIPIL Vol 11, No 2 (2022): Volume 11 Nomor 2 November 2022
Publisher : Jurusan Teknik Sipil, Fakultas Teknik, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jts.v11i2.27728

Abstract

Fenomena urban heat island sebagai dampak dari gencarnya pembangunan infrastruktur di wilayah DKI Jakarta merupakan isu masif dalam penggunaan energi suatu bangunan. Dalam upaya konservasi energi, Pemerintah DKI Jakarta menerapkan peraturan mengenai OTTV. Gedung Kantor Pusat ASDP Indonesia Ferry telah menerapkan teknologi double skin facade (DSF) untuk mencapai nilai OTTV yang sesuai dengan standar. Penelitian ini berfokus pada optimasi material DSF di Gedung Kantor Pusat ASDP Indonesia Ferry dengan tujuan mereduksi perolehan OTTV dengan biaya yang efisien. Dalam penelitian ini digunakan metode kuantitatif dengan menganalisis capaian nilai OTTV dengan acuan SNI 03-6389 dan Pergub Provinsi DKI Jakarta No. 38 Tahun 2012. OTTV yang diperoleh dari DSF eksisting adalah sebesar 61,86 W/m2. Penambahan dinding parapet beton dengan cat putih semi-kilap dapat mengurangi nilai OTTV secara signifikan hingga sebesar 23% menjadi 47, 46 W/m2 dengan harga yang efisien, sehingga menjadi alternatif modifikasi jangka pendek. Kombinasi modifikasi menggunakan kaca sunergy low-e blue green (8 mm) dan dinding parapet beton dengan cat putih semi-kilap merupakan alternatif modifikasi jangka panjang yang direkomendasikan. Modifikasi tersebut dapat menurunkan nilai OTTV sebesar 28% menjadi 44,65 W/m2 dengan biaya yang paling efisien.
Comparing MLP and 1D-CNN Architectures for Accurate RUL Forecasting in Lithium Batteries Assagaf, Idrus; Sukandi, Agus; Jannus, Parulian; Prasetya, Sonki; Apriana, Asep; Edistria, Ega; Abdillah, Abdul Azis
Recent in Engineering Science and Technology Vol. 3 No. 04 (2025): RiESTech Volume 03 No. 04 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i04.127

Abstract

Accurately forecasting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for optimizing battery management and ensuring operational reliability. This study compares the performance of two deep learning architectures—a Multilayer Perceptron (MLP) and a one-dimensional Convolutional Neural Network (1D-CNN)—in predicting RUL using datasets from CALCE batteries B35, B36, and B37. Data preprocessing involved outlier removal, missing value handling, and feature normalization, with key features extracted including Resistance, Constant Voltage Charging Time (CVCT), and Constant Current Charging Time (CCCT). Correlation analyses confirmed strong relationships between these features and RUL. Both models were trained and validated on preprocessed data, and their predictive accuracies were assessed using Root Mean Square Error (RMSE) and coefficient of determination (R2). Results indicated that while both architectures effectively captured battery degradation patterns, the MLP consistently outperformed the 1D-CNN, achieving on average 5% lower RMSE and 1.5% higher R2 across all tested batteries. These findings suggest that simpler fully connected networks may suffice for this forecasting task under the given feature set and preprocessing conditions. This work provides valuable insights into neural network model selection for battery health prognostics, guiding the development of efficient and accurate predictive maintenance strategies.
Evaluation of Civil Engineering Students’ Academic Performance Using Fuzzy C-Means Clustering Saputra, Jonathan; Edistria, Ega; Wacono, Sidiq; Sari, Tri Wulan; Adyan, Faqih Al
Jurnal Pendidikan Teknik Sipil Vol. 7 No. 2 (2025): November
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpts.v7i2.89018

Abstract

Background: Students’ academic performance is a crucial indicator of their mastery of core competencies obtained throughout the learning process in higher education. These competencies become an essential benchmark, not only for academic evaluation, but also for the industry that expects graduates to meet professional standards. Therefore, an objective and data-driven evaluation method is needed to identify students’ academic performance and support academic decision-making. Methods: This study employs the Fuzzy C-Means (FCM) clustering method as an educational data mining technique to classify civil engineering students based on their academic results. Three key competency areas are used in this study, i.e., Structure and Material (SM), Geometry and Transportation (GT), and Construction Management (CM). A total of 221 students were analysed, exceeding the minimum sample size. The clustering process was performed using multiple cluster models (three, four, and five clusters), and the silhouette coefficient was used to evaluate the quality and accuracy of the clusters. Results: The findings reveal that the three-cluster model provides the most representative structure, showing the highest silhouette coefficient value compared with others. This indicates that three clusters offer the most appropriate grouping for evaluating academic performance. Cluster 1 represents students with excellent academic achievement, cluster 2 consists of students with good performance, and cluster 3 represents students with concerning academic performance requiring additional academic support. Conclusion: Overall, the study concludes that the three-cluster model, consisting of an excellent, good, and concerning performance group, offers the most accurate and representative evaluation of civil engineering students’ academic performance. These results provide valuable insights to design targeted interventions, enhance learning support, and optimize curriculum alignment to ensure that students achieve the competencies required before entering the professional field.
Co-Authors Abdillah, Abdul Azis Adyan, Faqih Al Agus Sukandi Anis Rosyidah Asep Apriana, Asep Assagaf, Idrus Dewi Yanti Liliana DYAH NURWIDYANINGRUM Jannus, Parulian Kamil, Raihan Kirana, Puspanendah Sasotya Muzakki, Zahran Nugroho Rezakusuma, Muhammad Samsul Arifin Saputra, Jonathan Sidiq Wacono Sonki Prasetya Tri Wulan Sari, Tri Wulan Wijaya, Bunga Shafira